the role of school board social capital in district governance: effects on financial and academic...
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The Role of School Board Social Capitalin District Governance: Effects onFinancial and Academic OutcomesArgun Saatcioglu a , Suzanne Moore b , Gokce Sargut c & Aarti Bajaj aa University of Kansas , Lawrence, Kansas, USAb West Chester Area School District , West Chester, Pennsylvania,USAc Governors State University, University Park , Illinois, USAPublished online: 22 Jan 2011.
To cite this article: Argun Saatcioglu , Suzanne Moore , Gokce Sargut & Aarti Bajaj (2011) The Roleof School Board Social Capital in District Governance: Effects on Financial and Academic Outcomes,Leadership and Policy in Schools, 10:1, 1-42, DOI: 10.1080/15700760903511780
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Leadership and Policy in Schools, 10:1–42, 2011 Copyright © Taylor & Francis Group, LLC ISSN: 1570-0763 print/1744-5043 onlineDOI: 10.1080/15700760903511780
NLPS1570-07631744-5043Leadership and Policy in Schools, Vol. 10, No. 1, Oct 2010: pp. 0–0Leadership and Policy in Schools
The Role of School Board Social Capital in District Governance: Effects on Financial
and Academic Outcomes
School Board Social CapitalArgun Saatcioglu et al.
ARGUN SAATCIOGLUUniversity of Kansas, Lawrence, Kansas, USA
SUZANNE MOOREWest Chester Area School District, West Chester, Pennsylvania, USA
GOKCE SARGUTGovernors State University, University Park, Illinois, USA
AARTI BAJAJUniversity of Kansas, Lawrence, Kansas, USA
Social capital refers to the nature of ties within a social unit, aswell as the unit’s external relationships. We draw from organiza-tional sociology and political science, and also build upon existinginsights in school board research, to offer an approach thataddress the effects of bonding (internal ties) and bridging (exter-nal ties) by board members. We hypothesize that these two sourcesof social capital are positively associated with financial and aca-demic outcomes at the district level. Results, based on data from175 Pennsylvania districts between 2004–05 and 2006–07, sup-port our hypotheses. Implications for research and practice arediscussed.
Educational governance has undergone a significant paradigm shift in thelast few decades, in which the key concern has moved from a focus oninputs to one on outcomes, from a logic of confidence, in Boyd’s (1996)words, to a logic of consequences. The accountability challenge that hasaccompanied this shift has been intensified by recent initiatives such as the
Address correspondence to Argun Saatcioglu, University of Kansas, Educational Leader-ship and Policy Studies, 1122 West Campus Road, JRP 407, Lawrence, KS 66044, USA.E-mail: [email protected]
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No Child Left Behind Act (NLCB). While scholarly and policy debatesaddress the proper means to hold teachers, administrators, and schoolsaccountable for educational outcomes, similar concerns with regard toschool boards have been relatively rare. In fact, some have characterizedschool boards as obsolete entities that inherently deter reform (Whitson,1998; Gehring, 2003). Others have gone so far as to suggest that boards aredinosaurs left over from the agrarian past that do little more than contributeto inefficiency and political inertia (Chubb & Moe, 1990; Finn, 1991).
Yet, school boards remain one of the oldest and most legitimate grass-roots institutions in America (Wirt & Kirst, 2001) and they continue to play acentral role in democratic governance and in mediating between local com-munity preferences and broader state and federal policy choices (Iannacone& Lutz, 1970; Ehrensal & First, 2008). There appear to be two importantproblems underlying the issue of evaluating school boards for district out-comes. First, school boards are seemingly removed from key outcomes suchas student performance. Board practices and processes often have at best anindirect influence over the schools and students, since such effects are likelyto trickle down through several layers of implementation. It thereforebecomes a challenge not only to theoretically connect board dynamics tochanges in school and student outcomes, but to methodologically observesuch effects, since they may be relatively small (Alsbury, 2008a).
The second problem is that the quality of board member behaviors,interpersonal processes, and the board’s relationship to outside actors areoften treated as criteria for evaluating board success (for reviews, see Kow-alski, 2001; Land, 2002). While effective board functioning is a critical issue,it is not synonymous with “outcomes” and is therefore not necessarily indic-ative of board success. What is instead needed is a more explicit theoriza-tion and examination of the ways in which various aspects of boardfunctioning are related to specific district outcomes (Land, 2002).
In this article, we address these two problems by drawing on theconcept of social capital and by testing school board effects on district out-comes longitudinally. In particular, we draw on the theory of social capitalfrom mainstream organizational sociology and political science, a perspec-tive that establishes the conceptual basis for the link between the internaland external relations of group members on the one hand and concreteoutcomes related to group or organizational performance on the other(Granovetter, 1973, 1985; Coleman, 1990; Burt, 1992, 2000, 2005; Putnam,1993, 2000). According to this perspective, the social capital of a group isdetermined by bonding—the degree of cohesiveness, trust, and cooperationwithin the group—as well as by bridging, which has to do with the varietyand frequency of members’ ties to outside actors that increase the group’sresources, creativity, diversity, and capability. The higher the level of thesetwo sources of social capital, the more effective the group is likely to be interms of intended policy outcomes. We apply this perspective to school
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School Board Social Capital 3
boards and test it on data from 175 Pennsylvania districts between the2004–05 and 2006–07 school years.
Empirically, we ask whether bonding and bridging activities by boardmembers are instrumental in improving academic and financial outcomesat the district level? We aim not only to extend knowledge in the broaderfields of educational governance and policy, but more specifically to dem-onstrate the utility of the social capital concept in the study of school boarddynamics and effects. In terms of academic outcomes, we address the per-centage of eighth graders above proficient levels in reading in the district.Financially, we focus on current expenditures per student. Standardized testscores and current expenditures (much of which involves instructionalexpenses) are particularly important areas of district performance in theNCLB context, as many school systems struggle to comply with challengingAYP requirements as well as with the additional expenditures necessary toimplement various NCLB mandates (Wong & Nicotera, 2004; Sunderman,Kim, & Orfield, 2005; Gamoran, 2007). Accordingly, our study has specialrelevance for potential school board effects with regard to NCLB implemen-tation and other accountability initiatives.
THEORETICAL FRAMEWORK
Social capital broadly refers to the quality of networks and the structure ofsocial relations (Coleman, 1990; Krackhardt, 1990; Lin, 2001). The degree ofan actor’s social capital has to do with the intensity and scope of ties toother actors in close proximity as well as to those located at further reachesof the relevant environment (Burt, 2000, 2005). Such ties affect the level ofsupport the actor receives in pursuing goals. In Stanton-Salazar’s words,“The value of social capital, as a concept, lies in the fact that it identifiesproperties (or laws) of social structure that are used by actors to achievetheir interests” (1997, p. 8). Simply put, social capital is a “resource” thatfacilitates purposive action.
As opposed to this agency-oriented perspective, much of the educa-tional research on social capital has relied on a structural view of the con-cept, emphasizing the ways in which socially supportive and nurturingcircumstances in neighborhoods, families, and schools are beneficial for stu-dents and educators (for reviews, see Dika & Singh, 2002; Goddard, 2003;Horvat, Weininger, & Lareau, 2003). A similar theme has been fairly preva-lent in the more specialized fields of educational governance and policy aswell (Epstein, 1995; Driscoll & Krechner, 1999). One set of studies addressthe implications of community and family social capital for school improve-ment. These studies demonstrate the potential role of effective collaborationamong school administrators, parents, neighborhood action groups, andresourceful organizations and leaders in the broader community (such as
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foundations or universities) in accomplishing innovation and change at theschool level (e.g., Smith & Wohlstetter, 2001; Kahne et al., 2001; Chhuonet al., 2008; Miller & Hafner, 2008). Such network structures are also poten-tially beneficial for performance at the student level (Sanders, 2006). InMawhinney’s (2002) words, there is a “microecology of social capital”around the schools, which could contribute considerably to the organiza-tion of the schools as well as the success of students.
Another group of studies focuses on the role of social capital within theschools. A common area of interest has been the role of collaborative inter-actions among parents, building administrators, and teachers in implement-ing reforms. The more frequent these interactions, and the morecharacterized they are by trust and loyalty, the more successful and longlasting the reforms (Hoy & Miskel, 2001; Bryk & Schneider, 2002; Louis,2003; Daly & Chrispeels, 2005). Schools that are able to activate their socialcapital in this manner nurture a sense of collective efficacy that facilitatesthe achievement of organizational goals (Smylie & Hart, 1999; Hoy, Sweet-land, & Smith, 2002; Spillane, 2003). They also improve organizational com-mitment and performance among teachers (Tschannen-Moran, 2003, 2004;Scribner et al., 2007), as well as students’ academic performance (Goddard,Tschannen-Moran, & Hoy, 2001).
A third and relatively less prevalent theme of research deals withdynamics at the individual level. Studies in this area typically address theeffects of professional networks on the occupational choices of teachersand other school administrators. Scholars have shown, for instance, thatbroader network relations strongly predict career mobility for teachers(Thomas, 2007; Quartz et al., 2008), as well as principals and superinten-dents (Ortiz, 2001). Simply put, this line of research demonstrates the pri-vate benefits of social capital for individual educators.
School Boards, Social Capital, and Educational Governance
Applications of the concept of social capital to district governance havebeen rare. One way to utilize the concept in this regard is to adopt anagency-oriented view and to cast the school board as a discrete unit of anal-ysis. How does a school board develop social capital? And, what is the stra-tegic utility of the board’s social capital in terms of specific districtoutcomes, particularly improvement outcomes that are important objectivesof district governance? Addressing these questions primarily requires a con-ceptual basis on which to draw testable links between district outcomes andspecific board practices and processes. Insights from mainstream organiza-tional sociology and political science are instrumental for this purpose.
Relevant research in these two areas emphasizes the internal and exter-nal relationships of formal work groups and public bodies as sources ofsuch groups’ social capital, and refers to this group-level resource to predict
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School Board Social Capital 5
performance-related outcomes. School boards constitute an importantempirical case for social capital theory since these public bodies are increas-ingly criticized for internal dysfunctions that mitigate efficiency and policycoherence at the district level (Mitchell & Badarak, 1977; Danzberger 1992,1994; Danzberger & Usdan, 1994) and for their lack of productive ties tooutside actors, which tends to limit innovation, legitimacy, and resources forthe schools (Carol et al., 1986; Land, 2002; Peterson & Fusarelli, 2005; How-ell, 2005). Internal ties within a board are likely to contribute to social capi-tal at the group level when they are characterized by tight-knit connectionsthat facilitate trust, cooperation, and mutuality among board members. Put-nam (1993, 2000), Coleman (1990), and Burt (2000, 2005) refer to this con-dition as bonding (i.e., cohesion or closure), which enables agility andharmonious functioning for the entire group. Bonding is particularly impor-tant for public groups whose members are democratically elected to repre-sent a variety of interests. External ties, on the other hand, create socialcapital for a board when members frequently interact with a diverse set ofoutside actors, such as state and federal agencies, local businesses, commu-nity action groups, civic leaders, officials from other districts, and membersof various political and community institutions. Granovetter (1973, 1985)and Burt (2000, 2005) describe such a pattern of external ties as bridging (orbrokerage) because it nurtures original thinking in the group and providesthe group the capability to collect new information and draw on tangibleand intangible support from the outside.
Empirical support for the beneficial consequences of bonding andbridging comes from a variety of domains and levels of analysis. Putnam et al.(1983), for instance, found that, in pluralistic political systems, formal com-mittees, task groups, and state agencies where internal relations are charac-terized by high levels of bonding tend to achieve more effective policyoutcomes because member ties prevent opportunism and support consensus.In her study of management groups in the Silicon Valley, Saxenian (1994)has found that success—both financially and in terms of organizationalinnovation—was largely a function of harmonious relations within thegroups, coupled with effective knowledge sharing and coordination acrossgroups operating in different organizations. Likewise, Ancona and Caldwell(1992) demonstrated that successful project teams in business organizationswere those where the quality of within-team exchange and the functionaldiversity of members were high (for similar findings on corporate R&Dunits, see Reagans & Zuckerman, 2001; Reagans, Zuckerman, & McEvily,2004). Finally, taking a macro-historical approach, Barkey (2008) has dem-onstrated the role of bonding and bridging in the growth of empires. Sheargues that empire-building tends to succeed when members of the rulingelite not only engage in cooperative behavior to pursue their collectiveinterests, but more importantly, develop diverse ties with the elite fromother states and empires.
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Below, we apply our social capital perspective to school boards bycontextualizing the ideas of bonding and bridging in the existing literatureon district governance, and by elaborating on the potential reasons why thethese two sources of social capital are likely to result in improved academicand financial outcomes at the district level.
BONDING
School board research has consistently indicated that board members toooften function as “representatives” of contending constituencies or specialinterests, or champions of a single or a narrow set of personally compellingissues, rather than “trustees” charged with developing common goals andpolicies that reflect the shared values and interests of the district as a whole(McGonagill, 1987; Danzberger & Usdan, 1994; McCloud & McKenzie, 1994;Ehrensal & First, 2008). When members treat the board as a realm of com-petition among conflicting interests and are unwilling or unable to relate toone another in a constructive manner, the board is likely to function as a sti-fling “bureaucratic layer,” rather than an instrumental agency that contrib-utes to district progress (Mitchell & Badarak, 1977; Wirt & Kirst, 1982).School board effectiveness is also undermined by an implicit understandingof membership primarily as a means to advance one’s own political career(Wagner, 1992; Wilson, 1994). Viewing his or her membership in this man-ner, a board member would be less likely to invest the time and energynecessary to develop productive relations with other members (McAdams,2006).
Findings from various district-level case studies and surveys suggestthat board members, educational administrators, teachers, and members ofthe public perceive the inability of the school board to work as a team andthe undue influence of both special and personal interests as central imped-iments to effective governance (for a review, see Land, 2002). The challengebefore many board members is, therefore, to transcend interpersonal con-flicts and narrow interests, as well as their potential indifference to the col-lective progress of the district, and to invest in their relationships with oneanother.
Such an investment could transpire in terms of the structural, rela-tional, and cognitive features of board members’ relations. These, accordingto Nahapiet and Goshal (1998), are the three basic facets of bonding thatgenerates social capital within a group. The structural facet is related to thefrequency of information sharing among members. Information flows notonly stimulate openness and learning within the group, but also occasionreflective dialogue and collaborative discussions (Sparrowe et al., 2001).Since board governance is inherently an interpersonal and knowledge-intensive process, a high frequency of information sharing and exchangeamong members is likely to improve effectiveness and the collective
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capability to achieve results. The relational aspect of bonding is, on theother hand, associated with the level of trust that develops as a result ofgenuine interactions over time. Trust brings about efficient collaborationand coordination in the absence of explicit mechanisms to foster and rein-force such behaviors (Coleman, 1990; Onyx & Bullen, 2000). Given thepotential for political conflicts in a school board, trust is likely to counteractthe fear of opportunism and self-interest, and be beneficial both for theboard as a whole and for the members individually (Rousseau et al., 1998).Finally, the cognitive aspect of bonding refers to the shared vision amonggroup members. According to Leana and Pil (2005), the unity of goals andthe collectively held values that underlie them help create a sense of sharedresponsibility and joint action. When members of a group commit to a setof common goals, the problems of free-riding, indifference, and circumven-tion are potentially reduced, allowing more effective deliberations on themeans to achieve the goals and on the criteria for evaluation (Portes, 1998).In this regard, school board members who are able to collectively articulatea shared vision are also likely to enhance the board’s capacity to developand implement policy more efficiently and to assess the level of progressmore consistently.
There is also a growing consensus among organization theorists regard-ing the mutually amplifying effects of the structural, relational, and cognitivefacets of bonding (e.g., Leana & Van Buren, 1999; Adler & Kwon, 2002).Trust, for instance, potentially improves both the amount and the value ofshared information (Bradach & Eccles, 1989). Similarly, shared informationis likely to facilitate the adoption of a shared vision and the transmission ofcommon values (Mohammed & Dumville, 2001).
BRIDGING
Reliance on strong ties with group members may in itself result in confor-mity to a degree that is counterproductive (Uzzi, 1996; Portes, 1998). Perfor-mance is, therefore, also a function of weak, external connections. Theprecursor of this approach was Granovetter’s (1973) influential study show-ing that individuals have a better chance of finding employment throughtheir weak ties (acquaintances, or people they met along the way), com-pared to their close friends and family. By definition, close-knit networksare characterized by redundant ties since everyone in the group is familiarwith everyone else. Bridging, on the other hand, is accomplished whenmembers of a group reach outside to others who have new resources thatthe group does not already possess based on their existing connections.
External ties are important not only in terms of exposure to new, inno-vative ideas (Burt, 2005) and stable exchange of resources (Granovetter,1985), but also in terms of forming alliances (Leana & Barry, 2000), managinguncertainty (Useem et al., 1997), and securing legitimacy in the eyes of
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external stakeholders (Pfeffer & Salancik, 1978). There are a number ofloosely connected insights in school board research that demonstrate thebenefits of bridging by board members. For example, school boards thatinteract often with community organizations and institutions are found to bemore effective in policy development and implementation (Ziebarth, 1999;Lau, 2004). Given that various community concerns are likely to be voicedby a pool of civic organizations, such as parent and neighborhood groups,foundations, ethnic and cultural advocacy groups, and religious organiza-tions¸ board members’ connections with such actors are important for bothreceiving and providing feedback on key policies, as well as for musteringhelp and support for the school system as whole (Goodman & Zimmerman,2000; Resnick, 1999).
Board relations with other social service and local government agenciesare also important because members’ failure to frequently interact with suchactors undermines innovative ways to align educational services with otherservices, such as in health, housing, and transportation (Campbell &Greene, 1994; Shannon, 1994; Fusarelli, 2008). As Boyd (1996) notes, theprovision of integrated educational and social services is increasinglyregarded as crucial for long-term progress in education, since the nonschoolcircumstances of students profoundly affect their performance. By the sametoken, board connections with government agencies, including those at thestate level, are instrumental in the exchange of new ideas on educationalreform and policy implementation, as well as securing financial and politicalsupport for local board activities (Carol et al., 1986; Danzberger, 1992).Though research on the benefits of such linkages for student achievementremains thin, financial and regulative outcomes are likely to improve morereadily as a result of interactions with state and local agencies, especially asdistricts become more dependent on those agencies fiscally and in terms ofevaluation (Land, 2002). As external evaluation becomes more frequent,connections to outside actors could provide the board with the opportunityto align its curriculum to the prevailing standards of assessment and cre-atively implement associated reforms (Usdan, 1994).
Another area where bridging by board members is likely to be benefi-cial is public relations. There is good indication that frequent school boardinteractions with opinion leaders, particularly the members of the media,improve public approval for the board’s policies and its members(McLaughlin, 2002). Such interactions also tend to foster public participationin levies, board elections, and other democratic processes and rituals (Ian-naccone & Lutz, 1994; IASB, 2000). Last but not least, there is an increasingemphasis among scholars on the value of school board and broader districtinteraction with local businesses and universities (Levine & Trachtman,1988; Henig et al., 1999; Chhuon et al., 2008). Board member relations withprivate corporations are a potential source of innovative strategies toimprove district governance and school organization (Antelo & Henderson,
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1992; Scales et al., 2005), opportunities for financial support for the schools(Trachtman, 1985), and curricular adjustment and career choices for stu-dents (Hughes, Bailey, & Karp, 2002). Likewise, the board’s interactionswith universities could be beneficial in terms of new ideas for educationalorganization, curricular and academic progress, and teacher education(Teitel, 1999; Fullan, 1995; Chrispeels & Gonzales, 2006).
School Board Social Capital and Key District Outcomes in the NCLB Era
In her extensive review, Land stresses that school board research is “rifewith conclusions and recommendations based on personal experience,observations, and opinions” (2002, p. 265). Most studies, she argues, relyheavily on anecdotal evidence and fail to properly operationalize key vari-ables. Stringfield goes so far so to suggest that “the shallowness of researchon [school board outcomes] almost defies belief” (2008, p. 287). As wenoted in our introduction, the problem appears to be related to the lack of atheoretical perspective that connects board processes and practices to con-crete outcomes for schools and students, as well as of methodologicalapproaches capable of observing relevant effect sizes. Another problem isthe treatment of board processes and practices as measures of board effec-tiveness in and of themselves, rather than as predictors of district outcomes.As Cistone (2008) notes, while successful policy formulation and implemen-tation are significant issues to examine, they are not the same as effectivepolicy outcomes for districts, and could instead be viewed as predictors ofoutcomes (see also Mountford, 2008).
We focus on the effects of school board social capital on studentachievement and current expenditures at the district level. Although schoolboard effects may be construed in a variety of ways, improvements in stan-dardized test scores have, for better or worse, become a critical yardstick bywhich boards are evaluated for effectiveness in the NCLB era, both in theofficial policy arena and in the public mind (Henderson, Saks, & Wright,2001; Howell, 2005). Bonding and bridging by board members are likely toresult in improved student achievement for a variety of reasons. First, trustand a shared vision on the board can help focus the district’s resources andenergies to pursue NCLB-mandated achievement targets and curricularalignments. A common sense of priorities and basic goals on the part oflocal agencies and actors is a critical factor in the local implementationof many ambitious federal initiatives (Selznick, 1984/1949; Pressman &Wildavsky, 1984). Second, boards with high levels of bonding are likely tobe more effective in monitoring district progress and in holding individualschools accountable for student performance. And third, boards with highlevels of bridging can help improve student performance because they arelikely to be more successful in managing the ambiguities in implementing
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NCLB mandates. As Loveless (2007) points out, NCLB is an inherently pecu-liar policy that lacks explicit guidelines on ways to implement and achieveseveral of its mandates (see also Balfanz et al., 2007). This not only createsan information vacuum where local educational leaders struggle to findeffective means to put NCLB into practice, but offers opportunities forcreativity and customization of various mandates that the policy involves(Stover, 2009). Bridging by board members is likely to be beneficial in thisregard as external ties to formal agencies, other districts, public groups, anduniversities, and other outsiders are potential sources of information andinnovation, as well as support.
There are emerging patterns in school board research that are consistentwith our predictions about social capital effects on financial and academicoutcomes. Among the few systematic examinations of school board effects onachievement, the Iowa Association of School Boards’ Lighthouse Study (IowaAssociation of School Boards, 2000) demonstrated that changes in boardmember attitudes and governance practices resulted in a moderate improve-ment in reading comprehension within a span of two years. More recently,Alsbury (2008a, 2008b) provided more rigorous evidence that lower rates ofmember turnover were associated with higher levels of English and mathscores at the district level. Alsbury explains that high turnover among boardmembers is a sign of instability, which is commonly due to conflict and poorinterpersonal processes, as well as the disruptive politics of board-communityconnections. His study, therefore, hints at the importance of bonding andbridging by the school board. In some important ways, our study builds onand expands Alsbury’s work by directly addressing the nature and effects ofthe board’s internal and external relationships.
Similar to student performance, current expenditures—which refers toall areas of spending except capital, debt service, and transportation (Dun-combe & Yinger, 2005)—is an increasingly important district outcome forschool boards, particularly in the contemporary NCLB context. Broadly, thelevel of current expenditures per student in a district reflects the commu-nity’s willingness and ability to support public education (Gallaudet, 2003).These are critical factors in the NCLB era, since the current level of NCLBfunding does not even cover the costs of its own requirements (Rebell &Wolfe, 2008). Two lawsuits—one brought by the National Education Associ-ation and a number of school districts1 and the other by the State ofConnecticut2—have challenged the federal government’s failure to ade-quately fund NCLB and to reimburse the states and local districts for thecosts of implementation. Nearly 80 percent of districts nationwide reportthat they are having to absorb the new costs (Jennings, 2007). In a recentanalysis, Duncombe et al. (2008) have determined that the prevailing levelsof federal funds (for instance, through Title I) are only sufficient to supportvery low achievement standards. The authors argue that the level of currentexpenditures per student necessary to achieve the high proficiency levels
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mandated by NCLB require school officials to rely on additional revenuefrom local property tax, state aid, donations, and grants.
We hypothesize that both bonding and bridging by school boards arevaluable in this regard. Given its implications for effective group processes,bonding is likely to contribute to members’ ability to work collectively onstrategic increases in local funding (for instance, by means of new tax leviesand donations) as well as to develop a common understanding of how andwhere to allocate resources in order to pursue NCLB-required proficiencylevels. Bridging, on the other hand, would help justify funding increasesand new spending levels with external actors, perhaps even convince cer-tain resourceful actors besides the public (such as foundations or busi-nesses) to actively contribute funds to the schools. In addition, bridging islikely to keep the board open to external feedback on financial strategiesand to original ideas on creative ways to seek new funds and to increasecurrent expenditures.
While our study does not examine the relationship between expendi-tures and student performance—the evidence on which remains rather thin(Greenwald, Hedges, & Laine, 1996)—increased expenditures are likely tofacilitate the district’s implementation of NCLB mandates, such as increasedteacher quality, smaller class sizes, professional development programs forteachers and staff, tutoring for low-performing students, and periodic testingof all students. We view the board’s social capital as instrumental in generat-ing additional resources to accomplish these important policy componentsat the district level. Figure 1 shows our basic model.
METHODS
Empirical Context and Data Collection
Our study relies on survey and administrative data from Pennsylvaniadistricts between the 2004–05 and 2006–07 school years. Though we were
FIGURE 1 A Theoretical Model of Social Capital’s Influence on District Financial andAcademic Outcomes.
School BoardSocial Capital Activities
. Bridging
. Bonding
Financial Outcome
.
Academic Outcome
.
+
+Average StudentPerformance
Current Expenditure per Student
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12 Argun Saatcioglu et al.
limited to a single state, this simplified our analysis, given considerablecross-state variation in laws regarding school budgets, board structure, andother board operations. The data collection involved two separate steps.
First, in fall 2004, we sent out to all 501 school board chairs in the statea questionnaire, which included scales regarding the board’s overall socialcapital. We discuss each scale in further detail below. A hard copy of thequestionnaire was mailed, along with a stamped return envelope, and anofficial endorsement notice from the Pennsylvania School Boards Associa-tion (PSBA), which helped obtain the contact information for each district.PSBA also emailed a reminder notice to the board chairs four weeks afterthe questionnaire was mailed. We received a total of 185 responses over aperiod of eight weeks, ten of which were unusable due to a high level ofmissing values, leaving 175 responses (35 percent). The two large urban dis-tricts, Philadelphia and Pittsburgh, did not respond to our questionnaire. Touse official designations, about four percent of the responding districts were“mid-size cities,” 11 percent were “small towns,” 50 percent were “urbanfringes” of either mid-size or large cities (i.e., inner- and outer-ring suburbs),and 35 percent were “rural” (either inside or outside of metropolitan statisti-cal areas or [MSAs]). We consider our sample adequately representative ofthe pool of districts across Pennsylvania (see Table 1 for state-sample com-parisons of means and standard deviations for all study variables other thantax price and the social capital variables included in the questionnaire).
Obtaining individual questionnaire responses from all board members,rather than just from the chair, likely would have improved the quality ofour data. However, this strategy proved logistically difficult (requiringnearly 5,000 questionnaires) and received only limited support from thePSBA. We, therefore, treated the chair as a “key informant” on board rela-tionships. A key informant is a strategically positioned member whose per-ceptions about group processes tend to be highly correlated to aggregateperceptions at the group level in small to midsize task groups (Kumar,Stern, & Anderson, 1993). We confirmed the validity of this assumption on arandom subsample of 15 school boards in pilot runs of our questionnaire.
In the second step, we collected financial and student performancedata for the 175 districts from which we had received usable questionnaireresponses, for the years 2004–05, 2005–06, and 2006–07. We provide moredetail on the relevant measures below. To collect the data, we relied prima-rily on information from the Pennsylvania Department of Education and theNational Center for Education Statistics.
Measures of Predictors
A full list of all the constructs and related items included in our question-naire is provided in the Appendix. In this section, we describe the sourcesand conceptual properties of the constructs.
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13
TA
BLE
1D
escr
iptiv
e St
atis
tics
and I
nte
rcorr
elat
ions
of St
udy
Var
iable
s.
Sam
ple
(N
= 1
75)
Stat
e (N
= 6
01)
Mea
nSt
d. D
ev.
Mea
nSt
d. D
ev.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(1)
Per
cent W
hite
0.90
90.
125
0.88
90.
171
1.00
0(2
) Per
cent w
ith C
olle
ge D
egre
e0.
245
0.14
30.
224
0.13
7−0
.150
**1.
000
(3)
Par
ent Su
pport for
Schoolin
g3.
318
0.77
90.
008
0.11
61.
000
(4)
Dis
tric
t Si
ze9.
741
2.15
59.
459
2.32
1−0
.297
***
0.45
6***
0.03
71.
000
(5)
Com
par
able
Wag
e In
dex
1.01
00.
089
1.00
10.
090
−0.3
61**
*0.
583*
**0.
014
0.53
9***
1.00
0(6
) Per
cent of Engl
ish
Langu
age
Lear
ner
s0.
109
0.02
50.
111
0.02
7−0
.221
***
−0.2
23**
*−0
.083
−0.1
92**
−0.0
271.
000
(7)
Per
cent of Sp
ecia
l Educa
tion S
tuden
ts0.
148
0.03
00.
150
0.03
1−0
.244
***
−0.2
99**
*−0
.144
−0.2
07**
*−0
.104
0.93
6***
1.00
0(8
) Pe
rcen
t on
Fre
e or
Red
uced
-Price
Lun
ch0.
277
0.15
90.
303
0.16
5−0
.361
***
−0.6
50**
*−0
.069
−0.3
94**
*−0
.497
***
0.41
2***
0.48
5***
1.00
0(9
) Per
cent of Fa
mili
es w
ith C
hild
ren
Age
d 6
to 1
70.
262
0.02
10.
263
0.02
40.
111
0.19
7***
−0.0
360.
280*
**0.
180*
*0.
026
−0.0
44−0
.209
***
1.00
0
(10)
Ln
(Int
er-G
over
nmen
tal A
id p
er S
tude
nt)
8.32
00.
449
8.40
50.
436
0.11
1−0
.776
***
−0.0
87−0
.632
***
−0.6
62**
*0.
322*
**0.
380*
**0.
767*
**−0
.111
(11)
Tax
Price
0.30
80.
073
−0.3
07**
*0.
202*
**−0
.122
0.35
1***
0.34
6***
−0.0
32−0
.070
−0.0
810.
321*
**(1
2) L
n (
Dis
tric
t A
ggre
gate
Inco
me
per
St
uden
t)11
.720
0.40
211
.652
0.37
8−0
.008
0.82
6***
0.09
80.
461*
**0.
639*
**−0
.271
***
−0.3
13**
*−0
.775
***
0.07
8
(13)
Form
al B
ridgi
ng
2.84
50.
847
−0.1
49**
0.00
50.
067
0.04
50.
048
0.05
30.
036
0.00
9−0
.113
(14)
Info
rmal
Bridgi
ng
2.56
51.
084
−0.0
25−0
.120
0.13
8−0
.105
−0.1
58**
−0.0
85−0
.090
0.10
8−0
.181
**(1
5) B
ondin
g_Sh
ared
Vis
ion
3.93
10.
811
−0.0
180.
139
0.40
5***
0.05
20.
030
−0.1
80**
−0.2
20**
*−0
.045
0.03
4(1
6) B
ondin
g_In
form
atio
n E
xchan
ge3.
383
0.83
20.
109
0.02
80.
402*
**0.
015
−0.0
11−0
.162
**−0
.195
**−0
.095
0.00
8(1
7) B
ondin
g_Tru
st3.
651
0.86
10.
088
0.06
80.
339*
**0.
017
−0.0
12−0
.171
**−0
.157
**−0
.116
−0.0
19(1
8) L
n (
Curr
ent Exp
enditu
res
per
St
uden
t), 20
04–0
59.
197
0.15
09.
195
0.14
8−0
.190
**0.
473*
**0.
115
−0.0
400.
433*
**0.
234*
**0.
195*
*−0
.172
**0.
022
(19)
Ln(C
urr
ent Exp
enditu
res
per
tu
den
t), 20
05–0
69.
120
0.15
09.
129
0.14
9−0
.065
0.42
6***
0.10
3−0
.123
0.34
3***
0.12
60.
097
−0.2
26**
*0.
001
(20)
Ln(C
urr
ent Exp
enditu
res
per
tu
den
t), 20
06–0
79.
971
0.13
89.
969
0.14
4−0
.140
*0.
528*
**0.
102
0.00
90.
446*
**0.
204*
*0.
167*
*0.
248*
**−0
.004
(21)
Per
cent of 8th
Gra
der
s A
bove
Pro
fici
ent in
Rea
din
g, 2
004–
050.
747
0.11
40.
667
0.12
40.
386*
**0.
590*
**0.
136
0.22
8***
0.27
6***
−0.4
88**
*−0
.533
***
−0.7
48**
*0.
117
(22)
Per
cent of 8th
Gra
der
s A
bove
Pro
fici
ent in
Rea
din
g, 2
005–
06
0.74
60.
113
0.68
90.
119
0.37
2***
0.59
4***
0.13
7*0.
236*
**0.
283*
**−0
.489
***
0.53
6***
−0.7
30**
*0.
115
(23)
Per
cent of 8th
Gra
der
s Above
Pro
fici
ent in
Rea
din
g, 2
006–
070.
791
0.09
80.
704
0.11
10.
419*
**0.
573*
**0.
090
0.20
2***
0.27
5***
−0.4
49**
*−0
.487
***
−0.7
61**
*0.
086
(Con
tin
ued
)
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14
TA
BLE
1(C
ontin
ued
)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(1)
Per
cent W
hite
(2)
Per
cent w
ith C
olle
ge D
egre
e(3
) Par
ent Su
pport for
Schoolin
g(4
) D
istric
t Si
ze(5
) Com
par
able
Wag
e In
dex
(6)
Per
cent of Engl
ish
Langu
age
Lear
ner
s(7
) Per
cent of Sp
ecia
l Educa
tion S
tuden
ts(8
) Per
cent on F
ree
or
Red
uce
d-P
rice
Lunch
(9)
Per
cent of Fa
mili
es w
ith
Child
ren A
ged 6
to 1
7(1
0) L
n (
Inte
r-G
ove
rnm
enta
l A
id
per
Stu
den
t)1.
000
(11)
Tax
Price
−0.2
23**
*1.
000
(12)
Ln (
Dis
tric
t A
ggre
gate
In
com
e per
Stu
den
t)−0
.827
***
−0.0
171.
000
(13)
Form
al B
ridgi
ng
−0.0
32−0
.042
0.02
91.
000
(14)
Info
rmal
Bridgi
ng
0.09
9−0
.035
−0.1
100.
311*
**1.
000
(15)
Bondin
g_Sh
ared
Vis
ion
−0.1
57**
0.00
40.
095
0.01
00.
139
1.00
0(1
6) B
ondi
ng_I
nfor
mat
ion
Exch
ange
−0.1
30−0
.097
0.07
4−0
.018
0.16
7**
0.74
4***
1.00
0(1
7) B
ondin
g_Tru
st−0
.171
**−0
.110
0.10
90.
018
0.09
80.
708*
**0.
842*
**1.
000
(18)
Ln (Curr
ent Exp
enditu
res
per
St
uden
t), 20
04–0
5−0
.221
***
−0.1
70**
0.48
3***
0.05
4−0
.055
−0.0
11−0
.005
0.01
21.
000
(19)
Ln(C
urr
ent Exp
enditu
res
per
St
uden
t), 20
05–0
6−0
.208
***
−0.2
01**
0.44
4***
0.18
0*0.
232*
0.13
2*0.
096*
0.13
8**
0.83
0***
1.00
0
(20)
Ln(C
urr
ent Exp
enditu
res
per
St
uden
t), 20
06–0
70.
311*
**0.
125
0.52
9***
0.20
9**
0.21
1**
0.18
8**
0.13
7**
0.23
3***
0.86
7***
0.78
4***
1.00
0
(21)
Per
cent
of 8th
Gra
ders
Abo
ve
Prof
icie
nt in
Rea
ding
, 200
4–05
−0.6
24**
*−0
.007
0.63
3***
−0.0
71−0
.086
0.15
5**
0.14
80.
196*
**0.
161*
*0.
211*
**0.
220*
*1.
000
(22)
Per
cent
of 8th
Gra
ders
Abo
ve
Prof
icie
nt in
Rea
ding
, 200
5–06
−0.6
29**
*0.
014
0.63
7***
0.17
5*0.
097*
0.14
3**
0.14
2*0.
194*
**0.
162*
*0.
234*
**0.
219*
**0.
804*
**1.
000
(23)
Per
cent
of 8th
Gra
ders
Abo
ve
Prof
icie
nt in
Rea
ding
, 200
6–07
−0.6
13**
*0.
037
0.64
7***
0.27
2***
0.19
6***
0.16
2**
0.15
7**
0.20
3***
0.15
3**
0.15
6**
0.20
2**
0.83
0***
0.84
3***
1.00
0
***S
ignific
ant at
0.0
10.
**Si
gnific
ant at
0.0
50.
*Sig
nific
ant at
0.1
00.
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School Board Social Capital 15
BONDING
To measure the nature of internal board relationships, we relied on anadapted version of Leana and Pil’s (2006) instrument, originally developedto study similar “within-group” relationships at the school level. Consistentwith the three facets of bonding described earlier, Leana and Pil’s instru-ment involves three separate sections, assessing the structural, relational,and cognitive features of member relations. Each section contains severalfive-point items (“strongly disagree” to “strongly agree”). The structural scaleincludes six questions on issues such as openness, honesty, frequency, andwillingness in information sharing. The relational scale includes five ques-tions regarding trust, addressing the degree of respect, integrity, “teamspirit,” and confidence among board members. Finally, the cognitive scaleasks four questions about shared vision on the board, addressing issuessuch as the similarity of ambitions, the extent of common views concerningthe district’s purpose, and the degree of equal participation in goal setting.
BRIDGING
External relationships of board members were measured by a scale origi-nally developed to capture the networking patterns of public managers(O’Toole, 1997). Meier and O’Toole (2003) used an adapted version of thisscale to study networking by superintendents. Our version was gearedtoward board members. On a seven-point scale (“never” to “daily”), weasked the chair to rate the board’s (including the members and chair him-self/herself) frequency of interaction with 12 different categories of externalactors, such as city officials, state legislators, community leaders, parentgroups, and universities. Though this scale ignores all aspects of interactionother than the mere frequency of contact (for instance, the reputation andthe substance of the relationship), it is consistent with the theoretical con-ception of bridging in the mainstream social capital literature (e.g., Burt,2005).
We used the bridging scale in two different ways. First, we inquiredabout external relationships of the board in terms of official capacity, mean-ing the interactions that occur in the formal sense, when the chair or mem-bers relate to outside actors in their “official capacity as board members”(see the Appendix). The responses, in this regard, capture the extent of formalbridging. We then changed the frame of reference to unofficial interactions,when the members relate to outside actors not in their official roles, but intheir roles as fellow community members. We call this informal bridging.The rationale underlying this distinction has to do with the simple insightthat board members are often connected to other actors by means of com-mon experiences in a different context, such as being fellow members of apolitical organization, a church, or a chamber of commerce, or simply as
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16 Argun Saatcioglu et al.
being neighbors in a particular residential area. Such relationships arehighly relevant in that once a person becomes a school board member, his/her “informal connections” are likely to become a resource to get thingsdone or acquire information or seek opinions on various issues. In somecases, those very connections play a role in stimulating the person to runfor public office in the first place. Such informal connections of a leader,Field (2003) argues, constitute the “transferability” of social capital. In otherwords, the social capital embedded in a connection that one has developedin a particular context may be appropriated for use in another context(Adler & Kwon, 2002)—such as a board chair’s reliance on his/her pastmembership in a local chamber of commerce to nurture the relationshipbetween the school board and the business community.
Outcome Measures
CURRENT EXPENDITURES PER STUDENT
Our financial outcome measure is the logged current expenditures per ADM(average daily membership) for 2004–05—2006–07 period. We obtained thedata for the districts in our sample from the Pennsylvania Department ofEducation (PDE).3 This expenditure measure includes six specific areas ofspending: instruction, student support, instructional support, school admin-istration, and operations and maintenance. As such, it is more comprehen-sive than alternative measures that focus exclusively on teacher spending(Falch & Rattso, 1999) and narrower than those that involve total spending(Stone et al., 2001; Miller, 1996). It should be noted that Pennsylvania has aschool finance system in which local districts have considerable influenceover revenues and spending, unlike in states in which the school financesystem is centralized (such as in Michigan and California).
STUDENT PERFORMANCE
The measurement for student performance was the percentage of eighthgrade students scoring advanced or proficient on the Pennsylvania Systemof School Assessment (PSSA) reading test for 2004–05, 2005–06, and 2006–07. The data were obtained from PDE.4 The PSSA, which has been imple-mented annually for grades 3, 8, and 11 since 2001, is based on NCLBstandards for reading and math. Its performance level descriptors areadvanced, proficient, basic, and below basic. The required threshold forNCLB is “proficient.” Students in eighth grade were chosen since thosestudents would have received the benefit of any reading educationimprovements since the early 2000s. Though the effects we examine maybe subject to variation across grades, we consider such differences beyondthe scope of our study.
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School Board Social Capital 17
Controls
RACE, HUMAN CAPITAL, AND “FAMILY” SOCIAL CAPITAL
We controlled for a variety of community characteristics. Race was mea-sured in terms of the percentage of white students in the district. Thoughwe had aimed at accounting for the likely correlation between nonwhiteracial composition and educational disparities, our race measure partly cap-tured rural-nonrural differences (i.e. rural disadvantage) in our data. Thiswas due to the considerable proportion of predominantly white rural dis-tricts in the data set (35 percent). Since we had no responses from theboards of predominantly nonwhite urban districts (and since there wouldhave only been a few of them in Pennsylvania anyway), the mid-size citiesand the suburbs represent the relatively diverse districts in the study.
The percentage of district residents with college degrees served as aproxy for human capital, which inevitably correlates with various financialand academic outcomes. We also included a measure of “family” social cap-ital, to account for district-level differences in social capital originating fromwithin the families, which is different from the way social capital is con-strued in this study, at the board level. This was important because the aver-age parental support for education, regardless of other districtcharacteristics, may influence both average resources and learning (Dika &Singh, 2002). Accordingly, we included in the questionnaire sent to theschool boards a six-item scale, asking about the extent to which parentswere informed and involved in district improvement efforts, and how muchthey trusted the district and participated in various decisions (5-pointresponse options ranged from “strongly disagree” to “strongly agree”; seethe Appendix). The scale was originally developed by MDRC, a prominentpolicy research organization, in collaboration with the Center for Researchon the Context of Teaching at Stanford University (see Porter & Snipes,2006).
DEMAND AND COST FACTORS
Basic insights from the school finance literature suggest that two particularsets of fiscally relevant factors ought to be considered in order to obtainunbiased estimates of social capital effects on expenditures. Some of thesecontrols pertain to the estimation of student performance as well. The firstset involves “demand factors,” which are, collectively, a monetary expres-sion of a community’s desire for greater student performance. They includetotal intergovernmental aid to the district, the district’s tax price, its incomelevel, and “taste” factors (Gramlich & Rubinfeld, 1982; Card & Payne, 2002).Intergovernmental aid was measured as the logged sum of state and federalfunding per student, the data for which were obtained from PDE.5 Tax price(or tax burden) refers to the additional local property tax necessary to pay
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18 Argun Saatcioglu et al.
for a one dollar increase in per student spending (Miller, 1996; Stevens &Mason, 1996). This measure reveals the extent to which the amount of ahousehold’s tax bill competes with the desire to spend for private goods.The higher the competition, the less a household will support school spend-ing. To measure tax price for the districts in our dataset, we relied on calcu-lations provided by Colburn and Horowitz (2003) and used informationobtained from PDE and National Center for Education Statistics (NCES).6
District income reflects the local revenue generating capacity forschooling. Naturally, higher income districts are likely to express a higherdemand for student performance in the form of greater local revenues and,subsequently, greater expenditures. As a measure, we used logged aggre-gate district income per student, which is the sum of the incomes divided byenrolled student population. The data were obtained from PDE.7
Finally, taste factors are related to the community’s natural proclivity tosupport its schools (Ferris, 1985). For instance, a district with a higher per-centage of families with children is likely to support its schools more than onewith a lower percentage. As a measure, we used data from NCES regardingthe percentage of district households with children aged six to 17.8
The second set of factors to control for are associated with the cost ofeducation in the districts. “Cost factors” include district size, the proportionof disadvantaged students, and the level of competitive wages in the districtcompared to the rest of the state (Andrews, Duncombe & Yinger, 2002;Duncombe & Yinger, 2008). Considerable variation across districts alongthese lines has implications for both expenditures and student performance.The data for district size and proportion of disadvantaged students wereobtained from PDE.9 District size was measured in terms of average dailymembership (ADM). The ADM distribution in our data was classified into 12categories, with larger intervals at the higher end in order to account foreconomies of scale (Baker & Duncombe, 2004).10 The measures for disad-vantaged students were the percentages of English language learners, spe-cial education students, and those on free or reduced-price lunch. For thelatter two, five-year means were used (2001–2006) to smooth out the highlevels of annual variation. For wage information, we relied on the compara-ble wage index (CWI) for the districts, higher levels of which indicatehigher competitiveness, which, to some degree, also suggest a higher costof living (Taylor & Glander, 2006). The data were obtained from NCES.11
RESULTS
Measurement Characteristics
We tested the validity of the constructs included in our questionnaire bymeans of factor analysis procedures. First, we fit an exploratory factor anal-ysis (EFA) model on all 44 items shown in the Appendix, extracting six
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School Board Social Capital 19
factors—three for bonding (information, trust, and vision), two for bridging(formal and informal), and one for parental support for education. Wedropped a total of six items, ending with 38, to minimize the cross-loadings(see the Appendix for the list of dropped items and the pattern of loadingsfor the items that were retained).
We then used the EFA solution to refine our measurement by means ofconfirmatory factor analysis (CFA). We specified all six constructs and theassociated items simultaneously, along with an additional latent factor toaccount for common method bias (CMB). The model was constrained forthe CMB factor to predict all manifest items in the model with equal load-ings and to have an equal covariance with all latent constructs, an approachdescribed by Podsakoff et al. (2003). We achieved an acceptable fit bydropping seven more items from the model (see the Appendix). The finalmodel produced the following fit statistics: c2 = 754.304, p < 0.01, NFI =0.914, IFI = 0.947, CFI = 0.945, RMSEA = 0.043. Although the c2 statistic wassignificant, other indicators of relative (e.g., CFI) and parsimony model fit(e.g., NFI) suggested that the hypothesized measurement model fit the datareasonably well. The pattern of item loadings and covariance structureamong the latent constructs indicated strong evidence for convergent anddiscriminant validity.
As a final step, we relied on the list of items included in the final CFAmodel to obtain Cronbach Alpha estimates for all six constructs as indicatorsof reliability. These estimates ranged from 0.757 to 0.946 (see the Appendix).The individual loadings within each scale were used to generate a weightedaverage from the raw data to represent an overall scale score. These wereused in our subsequent structural equation model (SEM) analysis.
It should be noted that the CFA not only improved our overall mea-surement, but sharpened our distinction between formal and informalbridging by board members. The 12 categories of external actors in ourbridging scale were distributed in a distinct manner between the formal andinformal bridging constructs. Specifically, formal bridging involved externalties to local business leaders, state legislators, local municipal leaders,seniors/retirees, youth group leaders, and officials from other school dis-tricts. By contrast, informal bridging involved ties to PTO/PTA leaders, civiccommunity leaders, faith community leaders, ethnic/minority group repre-sentatives, news media representatives, and leaders in higher education.Basically, the formal-informal distinction with regard to bridging reflected adifference not only in the medium of connection, but in terms of the actorcategories involved. This insight, as we will show, proved valuable whiletesting our expectations of social capital effects on district outcomes.
Table 1 shows the basic descriptives and correlations for all the vari-ables included in the study. A number of important patterns stand out in thecorrelations. First, positive correlations are observed among most social cap-ital variables, an important exception being formal bridging, which is not
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20 Argun Saatcioglu et al.
correlated to any of the bonding measures. Our two outcome measures—current expenditures per student and percent of above proficient eighthgraders in reading—are positively and significantly correlated as well. Also,the correlations among the control variables are largely in the expecteddirection. Note that percent white is negatively correlated with most of thecontrol variables and with current expenditures, as it partly reflects rural dis-advantage. It is positively correlated with reading. Furthermore, most—though not all—control variables are significantly correlated with the out-come measures, which is a preliminary verification of the statistical rele-vance of the controls. The social capital measures, on the other hand, are atbest modestly related to the outcomes in 2004–05, but the relationshipsgrow in consecutive years. Our SEM analysis examines these patterns from amultivariate approach.
Structural Equation Model (SEM) Analyses
We discuss results for three separate SEMs. In all three SEMs, the predictorsare measures from the 2004–05 school year. The outcomes in the first SEMare also from 2004–05. The outcomes in the next two are from 2005–06 and2006–07 respectively. These lagged SEMs provide a more robust examina-tion of causal predictions of social capital effects. A key advantage of SEManalysis is that it allows the estimation of effects on more than one outcomeat a time. Thus, the effects on each of our two outcome variables—currentexpenditures and reading—are estimated while controlling for the effects onthe other. In each SEM analysis, we fit two consecutive models, first onewith only the controls as predictors, and second including the social capitalmeasures. Therefore, the coefficient estimates are robust to key measures ofdistrict affluence and other characteristics. The results are shown in Table 2and 3. Since each consecutive model in either table is estimated simulta-neously with the corresponding model in the other table, we report fit sta-tistics for the models in both tables only in Table 2.
The effects on current expenditures per student are in shown Table 2.Broadly speaking, most controls influence current expenditures in theexpected direction, although some effects are non-significant. Educationlevel (percent with college degree) has a consistently high and significantinfluence. As expected, intergovernmental aid and district income per stu-dent increase current expenditures, while tax price decreases it, reflectingthe potential obstacle that a community’s tax burden puts on the procure-ment and allocation of resources. In addition, district size, an important costfactor, reduces current expenditures (most likely a result of economies ofscale), while comparable wage index tends to increase those expenditures.These patterns remain considerably similar across the years in Table 2. Itshould also be noted that omitting causal paths from all nonsignificantcontrols to current expenditures in the estimation of the models makes a
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21
TA
BLE
2St
andar
diz
ed S
EM
Est
imat
es o
f th
e Effec
ts o
f Sc
hool B
oar
d S
oci
al C
apita
l on C
urr
ent Exp
enditu
res
per
Stu
den
t, 20
04–0
5—20
06–0
7a .
Pre
dic
tors
2004
–05
2005
–06
2006
–07
Model
1M
odel
2M
odel
1M
odel
2M
odel
1M
odel
2
Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.
Com
mu
nit
y C
ha
ract
eris
tics
Per
cent W
hite
−0.1
02(0
.102
)−0
.104
(0.1
02)
−0.1
35(0
.117
)−0
.141
(0.1
17)
−0.0
85(0
.103
)−0
.060
(0.1
01)
Per
cent w
ith C
olle
ge
Deg
ree
0.47
4***
(0.1
05)
0.43
9***
(0.1
07)
0.51
2***
(0.1
21)
0.52
3***
(0.1
22)
0.55
6***
(0.1
06)
0.56
4***
(0.1
05)
Par
ent Su
pport for
Schoolin
g0.
042
(0.0
56)
0.03
4(0
.060
)0.
055
(0.0
64)
0.06
4(0
.945
)0.
058
(0.0
56)
0.07
2(0
.059
)
Dem
an
d F
act
ors
Inte
rgove
rnm
enta
l Aid
per
Stu
den
t0.
282*
**(0
.118
)0.
326*
**(0
.120
)0.
417*
**(0
.136
)0.
449*
**(0
.137
)0.
301*
**(0
.119
)0.
315*
**(0
.118
)
Tax
Price
−0.2
73**
*(0
.072
)−0
.249
***
(0.0
73)
−0.2
21**
*(0
.082
)−0
.197
***
(0.0
83)
−0.1
41**
(0.0
70)
−0.1
19*
(0.0
71)
Per
cent of Fa
mili
es w
ith
Child
ren A
ged 6
to 1
70.
044
(0.0
63)
0.03
7(0
.063
)0.
019
(0.0
72)
0.01
0(0
.072
)0.
010
(0.0
63)
0.00
7(0
.063
)
Dis
tric
t A
ggre
gate
In
com
e per
Stu
den
t0.
381*
**(0
.141
)0.
400*
**(0
.141
)0.
252
(0.1
62)
0.27
5*(0
.161
)0.
341*
**(0
.142
)0.
370*
**(0
.139
)
Cos
t Fa
ctor
sD
istric
t Si
ze−0
.281
***
(0.0
80)
−0.2
77**
*(0
.081
)−0
.309
***
(0.0
91)
−0.3
04**
*(0
.092
)−0
.315
***
(0.0
80)
−0.3
23**
*(0
.080
)Com
par
able
Wag
e In
dex
0.29
3***
(0.0
91)
0.30
1***
(0.0
92)
0.32
5***
(0.1
05)
0.32
3***
(0.1
05)
0.34
2***
(0.0
92)
0.34
5***
(0.0
91)
Per
cent of Engl
ish
Langu
age
Lear
ner
s0.
208
(0.1
66)
0.28
1*(0
.169
)0.
149
(0.1
91)
0.22
0(0
.192
)0.
232
(0.1
66)
0.31
7*(0
.167
)
Per
cent of Sp
ecia
l Educa
tion S
tuden
ts0.
112
(0.1
74)
0.02
7(0
.178
)−0
.076
(0.1
99)
−0.0
83(0
.202
)0.
023
(0.1
74)
−0.0
86(0
.175
)
Per
cent on F
ree
or
Red
uce
d-P
rice
Lunch
0.11
2(0
.135
)0.
139
(0.1
37)
0.05
2(0
.155
)0.
117
(0.1
56)
0.09
0(0
.136
)0.
146
(0.1
35)
Soci
al C
api
tal
Bridgi
ng_
Form
al0.
070
(0.0
69)
0.10
4*(0
.053
)0.
121*
*(0
.059
)B
ridgi
ng_
Info
rmal
0.06
4(0
.061
)0.
099*
(0.0
53)
0.15
5**
(0.0
76)
Bondin
g_Sh
ared
Vis
ion
0.12
7(0
.082
)0.
165*
(0.0
94)
0.17
3**
(0.0
81)
(Con
tin
ued
)
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22
TA
BLE
2(C
ontin
ued
)
Pre
dic
tors
2004
–05
2005
–06
2006
–07
Model
1M
odel
2M
odel
1M
odel
2M
odel
1M
odel
2
Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.
Bondin
g_In
form
atio
n
Exc
han
ge0.
063
(0.0
94)
0.18
1*(0
.098
)0.
183*
(0.1
07)
Bondin
g_Tru
st0.
174*
**(0
.069
)0.
196*
**(0
.077
)0.
226*
**(0
.065
)
Model
Fit
Stat
istic
sb
Chi-Sq
uar
e12
.486
(p =
0.0
29)
17.5
70 (
p =
0.0
07)
6.38
2 (p
= 0
.194
)6.
560
(p =
0.3
63)
9.37
8 (p
= 0
.095
)8.
272
(p =
0.2
18)
Goodnes
s of Fi
t In
dex
(G
FI)
0.98
90.
988
0.99
30.
995
0.99
20.
994
Norm
ed F
it In
dex
(N
FI)
0.99
30.
992
0.99
60.
997
0.99
50.
996
Non-N
orm
ed F
it In
dex
(N
NFI
)0.
914
0.93
30.
972
0.99
20.
951
0.96
7
Com
par
ativ
e Fi
t In
dex
(CFI
)0.
995
0.99
40.
998
0.99
90.
997
0.99
8
Root M
ean S
quar
ed E
rror
of Appro
xim
atio
n
(RM
SEA
)
0.09
90.
101
0.05
60.
025
0.07
60.
050
a Inte
rgove
rnm
enta
l ai
d a
nd d
istric
t ag
greg
ate
inco
me
mea
sure
s w
ere
logg
ed.
bThes
e fit st
atis
tics
apply
to r
esults
in T
able
3 a
s w
ell.
***S
ignific
ant at
0.0
10.
**Si
gnific
ant at
0.0
50.
*Sig
nific
ant at
0.1
00.
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23
TA
BLE
3St
andar
diz
ed S
EM
Est
imat
es o
f th
e Effec
ts o
f Sc
hool B
oar
d S
oci
al C
apita
l on t
he
Per
cent
of
Eig
hth
Gra
der
s ab
ove
Pro
fici
ent
in R
eadin
g,20
04–0
5—20
06–0
7a,b
Pre
dic
tors
2004
–05
2005
–06
2006
–07
Model
1M
odel
2M
odel
1M
odel
2M
odel
1M
odel
2
Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.
Com
mu
nit
y C
ha
ract
eris
tics
Per
cent W
hite
0.42
5***
(0.0
76)
0.43
3***
(0.0
74)
0.25
3***
(0.0
69)
0.25
3***
(0.0
68)
0.39
1***
(0.0
70)
0.39
3***
(0.0
64)
Per
cent w
ith C
olle
ge D
egre
e0.
436*
**(0
.092
)0.
385*
**(0
.091
)0.
346*
**(0
.083
)0.
323*
**(0
.084
)0.
269*
**(0
.084
)0.
235*
**(0
.084
)Par
ent Su
pport for
Schoolin
g0.
081
(0.0
50)
0.08
3(0
.052
)0.
053
(0.0
45)
0.03
8(0
.048
)0.
018
(0.0
46)
0.00
7(0
.045
)
Dem
an
d F
act
ors
Inte
rgove
rnm
enta
l Aid
per
St
uden
tTax
Price
Per
cent of Fa
mili
es w
ith
Child
ren A
ged 6
to 1
7D
istric
t A
ggre
gate
Inco
me
per
St
uden
t0.
064
(0.1
05)
0.09
1(0
.102
)0.
098
(0.0
94)
0.11
5(0
.094
)0.
181*
(0.0
96)
0.20
7**
(0.0
93)
Cos
t Fa
ctor
sD
istric
t Si
ze0.
067
(0.0
68)
0.06
7(0
.066
)0.
054
(0.0
61)
0.04
0(0
.062
)0.
055
(0.0
63)
0.07
6(0
.061
)Com
par
able
Wag
e In
dex
Per
cent of Engl
ish L
angu
age
Lear
ner
s−0
.398
**(0
.151
)−0
.309
**(0
.136
)−0
.314
**(0
.135
)−0
.246
*(0
.138
)−0
.338
***
(0.1
38)
−0.2
29*
(0.1
36)
Per
cent of Sp
ecia
l Educa
tion
Studen
ts0.
267*
(0.1
59)
0.17
2(0
.157
)0.
061
(0.1
42)
0.10
0(0
.146
)0.
185
(0.1
45)
0.09
1(0
.145
)
Per
cent on F
ree
or
Red
uce
d-P
rice
Lunch
−0.1
61(0
.103
)−0
.147
(0.1
00)
−0.2
82**
*(0
.092
)−0
.278
***
(0.0
93)
−0.2
44**
*(0
.094
)−0
.239
***
(0.0
82)
(Con
tin
ued
)
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24
TA
BLE
3(C
ontin
ued
)
Pre
dic
tors
2004
–05
2005
–06
2006
–07
Model
1M
odel
2M
odel
1M
odel
2M
odel
1M
odel
2
Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.Coef
f.St
d. Err
.
Soci
al C
api
tal
Bridgi
ng_
Form
al0.
048
(0.0
53)
0.09
0*(0
.049
)0.
103*
(0.0
57)
Bridgi
ng_
Info
rmal
0.09
1*(0
.054
)0.
079
(0.0
47)
0.14
1**
(0.0
64)
Bridgi
ng_
Shar
ed V
isio
n0.
105
(0.0
73)
0.13
4**
(0.0
67)
0.15
2**
(0.0
77)
Bridgi
ng_
Info
rmat
ion
Exc
han
ge0.
153*
(0.0
86)
0.20
9**
(0.0
92)
0.22
7**
(0.1
11)
Bondin
g_Tru
st0.
186*
**(0
.071
)0.
199*
(0.1
17)
0.22
6***
(0.0
65)
a Inte
rgove
rnm
enta
l ai
d a
nd d
istric
t ag
greg
ate
inco
me
mea
sure
s w
ere
logg
ed.
bThe
fit st
atis
tics
for
thes
e re
sults
are
show
n in T
able
2.
***S
ignific
ant at
0.0
10.
**Si
gnific
ant at
0.0
50.
*Sig
nific
ant at
0.1
00.
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School Board Social Capital 25
statistically significant difference in fit measures, indicating that the nonsig-nificant controls collectively explain a meaningful proportion of the out-come variance.12
Most importantly, the statistically significant effects of control variablestend to change negligibly from Model 1 to Model 2 for any given year. Sincewe include the social capital measures in Model 2, the relative stability ofcontrol variable effects indicate a lack of substantial interaction between thesocial capital measures and the controls. Thus, the estimated social capitaleffects are robust “main” effects of bonding and bridging.
As seen in the results for Model 2 in Table 2, most social capital mea-sures have nonsignificant effects on current expenditures per student in2004–05, except for trust (b = 0.174, p < 0.050). Both the effect of trust andthose of the other social capital measures improve in terms of size as well asstatistical significance for 2005–06. Formal and informal bridging effectsgrow by about 30 percent, though both remain significant only at the 0.100level. A similar pattern is observed for shared vision and informationexchange. The growing effects of school board social capital on the twooutcomes, reported in Table 2, are visually depicted in Figure 2. Model 2results for 2006–07 indicate that it takes about two years for the influence ofschool board social capital to become substantial in size and statisticallymeaningful. This is unsurprising since board relationships that help raisenew funds and/or reallocate existing resources in ways to increase currentexpenditures are likely to take some time to result in concrete changes.
Although the effects of 2004–05 external ties—both formal and infor-mal—on current expenditures in 2006–07 are small relative to the statisti-cally significant controls in that year, they provide important insights. Mostnotably, the effect of informal bridging (b = 0.155, p < 0.050) is about 22percent greater than that of formal bridging (b = 0.121, p < 0.050). This sug-gests that, as far as improving current expenditures is concerned, boardmembers’ informal relationships with PTO/PTA leaders, civic communityleaders, faith community leaders, ethnic/minority group representatives,news media representatives, and leaders in higher education are somewhatmore important that formal ties to local business leaders, state legislators,local municipal leaders, seniors/retirees, youth group leaders, and officialsfrom other school districts. We tested the difference statistically by fittinganother model where the formal and informal bridging coefficients areconstrained to be equal for 2006–07. The resulting fit measures for thisconstrained model were lower (statistically) than those for the uncon-strained model reported here in Table 2, suggesting that the observed differ-ence is meaningful.
As far as internal board relations are concerned, only shared vision andtrust on the board have statistically significant effects on 2006–07 currentexpenditures—also shown in Figure 2. Trust (b = 0.226, p < 0.010) has thestrongest influence, nearly 25 percent greater than that of shared vision
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26 Argun Saatcioglu et al.
(b = 0.173, p < 0.050). The effect of information exchange is bearly signifi-cant (b = 0.183, p < 0.100). Results from constrained models, where theeffects of all three bonding measures were forced to be equal, indicate thatthe unconstrained results reported here represent statistically superior fitmeasures. It appears that lack of opportunism and a common notion of
FIGURE 2 Cross-Sectional and Lagged Effects of School Board Social Capital on CurrentExpenditures per Student, 2004–05—2006–07 *** Significant at 0.050. *** Significant at 0.100.
Bonding effects
0.127
0.165* 0.173**
0.063
0.181*
0.183* 0.174***
0.196***
0.226***
0.000
0.050
0.100
0.150
0.200
0.250
2006–072005–062004–05
Shared Vision Information Exchange Trust
Bridging effects
0.070
0.104*
0.064
0.155**
0.000
0.050
0.100
0.150
0.200
0.250
2006–072005–062004–05
Formal Informal
0.099*
0.121**
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School Board Social Capital 27
district goals on the part of board members are significant factors in improv-ing current expenditures per student.
The effects on percent of eighth graders above proficient in reading areshown in Table 3. In our SEM analyses, we did not specify any effects onreading from intergovernmental aid, tax price, taste factors (percent of fami-lies with children ages 6 to 17), and comparable wage index, as these con-trols pertain largely to the estimation of current expenditures. As in Table 2,most of the remaining controls influence the outcome in the expected direc-tion, although some effects are nonsignificant. Unlike the previous analysis,omitting paths from nonsignificant controls to reading in the estimationdoes not result in statistically significant changes in fit measures.
As expected, percent white and percent with college degree substan-tially increase reading performance at the district level, while percent ofEnglish language learners and percent on free or reduced-price lunchdecrease it. Given the relatively high correlation between district incomeand percent on lunch (r = −0.775, p < 0.010; see Table 1), it is likely that theeffect of income is being picked up largely by the lunch variable in mostyears (removing the lunch variable or district income does not result inmeaningful changes in the observed social capital effects, which are ourcentral focus). In addition, similar to the findings in Table 2, the effect sizesof significant control variables remain largely the same from Model 1 toModel 2 for any given year, indicating that the social capital effects esti-mated in Model 2 are robust “main” effects of bonding and bridging.
In the cross-sectional SEM (2004–05) results in Table 3, the only socialcapital measure with a statistically significant effect on reading is trust (b =0.186, p < 0.010), while informal bridging and information exchange haveborderline significance. Both the effect sizes and the statistical significanceof the bridging and bonding measures form 2004–05 consistently grow overtime. The trajectory of these lagged social capital effects on reading is visu-ally depicted in Figure 3 below. As in the case of effects on current expen-ditures, it seems that internal and external board relationships that facilitateimprovements in student performance tend to take some time to result inconcrete changes. As seen in Model 2 for 2006–07 in Table 3, the size of theinformal bridging effect on reading (b = 0.141, p < 0.010) is about 40 per-cent larger than that of formal bridging, which remains at borderline signifi-cance (b = 0.103, p < 0.100). As far as the influence of bonding measures areconcerned, information exchange and trust have high and nearly identicaleffects on 2006-07 district reading performance (b = 0.227, p < 0.050 and b =0.226, p < 0.010), while shared vision has a 32 percent smaller but still sig-nificant effect (b = 152, p < 0.052). As before, the results from models wherethe effects of all three bonding measures were constrained to be equalsuggest that the unconstrained results reported here produce statisticallysuperior fit measures. In summary, informal ties to external actors—such asPTO/PTA leaders, civic community leaders, faith community leaders,
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ethnic/minority group representatives, news media representatives, andleaders in higher education—as well as a lack of opportunism (due to hightrust), high levels of information exchange and a common sense of district goalsare potentially important factors in improving average student achievement.
FIGURE 3 Cross-Sectional and Lagged Effects of School Board Social Capital on the Percentof Eighth Graders above Proficient in Reading, 2004–05—2006–07. ***Significant at 0.010.**Significant at 0.050. *Significant at 0.100.
2006–072005–062004–05
Bonding effects
0.105
0.209**0.227**
0.186***0.199*
0.226***
0.000
0.050
0.100
0.150
0.200
0.250
Shared Vision Information Exchange Trust
0.153*
0.134**
0.152**
Bridging effects
0.048
0.090*0.091*
0.079
0.000
0.050
0.100
0.150
0.200
0.250
2006–072005–062004–05
Formal Informal
0.103*
0.141**
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Altogether, the results thus far support our theoretical expectationsabout school board social effects on financial and academic outcomes at thedistrict level. In the following section, we reiterate our basic argument andour findings, and also elaborate on the potential processes underlying thereported findings. We also highlight the implications for future research andthe practical value of the social capital perspective for educational leaders.
DISCUSSION
In this article, we have examined the effects of school board social capitalon district financial and academic outcomes. Drawing on insights frommainstream organizational sociology and political science, we conceptual-ized the board’s social capital in terms of members’ bonding and bridgingactivities. The former refers to strong ties among members manifested in theform of shared vision, open/honest exchange of information, and trust,while the latter denotes weaker ties to key external parties, which are criti-cal for innovation, resources, and legitimacy.
Our approach extends research on educational policy and governancein two important ways. First, studies focusing on social capital typicallyaddress relevant processes and outcomes at the community, school-building,and individual levels, paying little attention to the specific implications ofthe concept for school boards. Studies focusing on board governance, onthe other hand, often consider a single or only a few aspects of either inter-nal or external ties of board members, rarely treating such ties as constitu-tive elements of a broader process of social capital. We offer a unifyingframework that sets the stage for more comprehensive inquiries concerningschool board social capital.
When applied to a formal group, such as a school board, the theory ofsocial capital suggests that bonding and bridging by members represent acollective resource that facilitates the achievement of the group’s centralobjectives. We therefore tested the influence of bonding and bridging ontwo district-level outcomes that are particularly important in the NCLBcontext—namely, current expenditures per student and the percentage ofabove proficient eighth graders in reading—with the expectation that bothwould be positively affected. Our results not only support our hypotheses,but point to a set of new questions and directions for further research.
Our major finding is that school board social capital indeed plays animportant role improving financial and academic outcomes. In the state ofPennsylvania, it takes about two years for these effects to manifest statisti-cally meaningful changes. The internal and external ties of board membersin 2004–05 tend to result in improvements in current expenditures and read-ing scores in 2006–07. We should point out that, since our data did notinclude information on the average tenure of board members or the time
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when they were elected, the analysis could not take into account the poten-tial variance in the maturity of social capital among the boards that weresurveyed. We, therefore, could not test whether boards that have recentlydeveloped social capital were any different in terms of outcomes whencompared to boards that have had social capital for longer periods. Whilethis is an important issue to examine in future studies, it does not under-mine our key insight regarding the lagged effects of social capital (regard-less of when created) on financial and academic outcomes at the districtlevel.
We also found that different aspects of board relationships tend to havedifferent implications for these district outcomes. First, bonding measures—shared vision, information exchange, and trust—appear to have largereffects on both expenditures and achievement than do the bridging mea-sures—external formal and informal ties. This suggests that functional andharmonious relations within the board are somewhat more important thanmember ties to outsiders for district outcomes. While is a finding that isessentially consistent with past research on social capital effects in educa-tional administration (Driscoll & Krechner, 1999), our results suggest that, atleast as far as school boards are concerned, emphasis on internal relation-ship alone, without sufficient attention to external ties may be insufficient inexamining and understanding social capital dynamics and effects.
Moreover, our findings provide important insights concerning the rela-tive weight of different bridging and bonding components with regard tofinancial and academic outcomes. For instance, as noted earlier in refer-ence to Figure 2, the lagged effect of informal bridging on 2006–07 currentexpenditures (b = 0.155, p < 0.050) is 22 percent greater than that of formalbridging on the same outcome (b = 0.121, p < 0.05). The size difference istwice as large (about 40 percent) for bridging effects on reading, as seen inFigure 3, where informal bridging effect is 0.141 (p < 0.050) and the formalbridging effect is 0.103 (p < 0.100). Thus, while formal board member ties tospecific external actors (e.g., businesses, legislators, etc.) do tend to matterin terms of both outcomes, informal ties to another group of actors (e.g.,PTA/PTO leaders, civic groups, etc.) appear to add more value to the sameoutcomes. As far as the bonding measures are concerned, the lagged effectof trust appears to be the most important one for improved current expen-ditures in 2006–07, followed by information exchange and shared vision,which have similar effect sizes (though one is only borderline significant;see Figure 2). By contrast, when the outcome is 2006–07 reading perfor-mance, trust and information exchange are equally important, followed byshared vision (Figure 3).
While the differences in the effect sizes of various bridging and bond-ing measures have important practical implications for board members andother educational leaders, the potential explanations of these differencespoint to interesting areas of further research. Why, for instance, are informal
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external ties more important than formal ones? What is it that transpires inthese two types of relationships that makes one type more impactful thanthe other? Extant research fails to provide a clear answer. Our study fails aswell, since we did not inquire about the substance of the ties, but simplyabout their frequency and diversity. In the same vein, what is it that makestrust so important for both financial and academic outcomes, while informa-tion exchange is much less important for one outcome than it is for theother? Similarly, why is shared vision less important than trust and informa-tion exchange for either outcome? One may speculate that, in the NCLBcontext, districts’ academic goals are increasingly defined by federal andstate agencies, limiting the diversity of purposes on the board. But, furtherresearch is ultimately necessary to examine such patterns.
By applying the social capital concept to the study of school boards,our study demonstrates that it is conceptually plausible to draw testablelinks between board practices and policies on the one hand and district out-comes on the other—particularly when the methodological approach allowsfor robust examination of lagged effects. While connecting high-leveladministrative dynamics to seemingly “distal” outcomes is instrumental infurthering knowledge in organizational research (Pfeffer & Salancik, 1978;Perrow, 1985; Scott, 2001), it also poses an interesting challenge concerningthe processes that constitute the hypothesized causal links. What, forinstance, do a board’s informal external ties produce to ultimately lead toimprovements in teacher pay, instructional innovations, and studentachievement? The same goes for the board’s formal external ties, as well asthe different elements of internal dynamics. Simply put, what exactly hap-pens in between school board social capital and district outcomes?
At this point, our study relies on sound theoretical and research-baseddeduction and argumentation to suggest possible process characteristics. Forexample, members may work well with one another, therefore put more effec-tive monitoring and accountability measures in place, and help implementNCLB mandates more successfully, resulting in higher achievement. Or, mem-bers’ informal ties to the media, universities, or civic groups may help publiclyjustify increases in or reallocation of school funds, which may in turn result inhigher current expenditures. Ultimately, though, our study does not answerwhat precisely happens so that these outcomes are brought about. Substantiat-ing the relevant process would not only further validate social capital effects,but more importantly help explain the associated causal links in a more com-prehensive fashion. It may also help illustrate a variety of mediating and moder-ating factors concerning social capital effects on district outcomes.
We would like to conclude by pointing to some of the limitations inour analysis that future studies can improve upon. First, we relied on ques-tionnaire responses only from board chairs. While we believe the associatedbiases were limited, a more elaborate data collection process could obtaindata from at least a cross-section of members on a given board; perhaps
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even from the superintendent as well, since superintendents often play animportant role in nurturing functional relationships among board members.Second, although we were able to fit models with lagged predictors and arich variety of control variables, a more sophisticated dataset with repeatedmeasures on all measures would allow a rigorous examination of the effectsof change in social capital on changes in district outcomes over time. Suchdata would also allow fitting more sophisticated models (e.g., using fixed-effects and instrumental variables approaches) that may validate our results fur-ther. Moreover, we would be able to observe recursive effects where outcomesat one point in time feed into social capital dynamics at a later time. The thirdlimitation, one that is specifically relevant for academic outcomes, was the lackof data on individual students. Individual test data can help fit multilevel mod-els examining the effects of school board social capital on individual studentperformance or on different groups of students. If the data is also longitudinal,multilevel growth models could also be fit to rigorously examine the role ofsocial capital on performance trajectories over time.
Fourth, our dataset lacked responses from urban districts, for whichschool board social capital may be particularly critical, since such districtstypically require more reform, progress, and genuine innovation than other,more affluent districts do. However, datasets limited to a single state, likeours, are unlikely to resolve the problem, since any given state will have nomore than a few large urban districts. Therefore, nationally representativedata from several states may be needed.
NOTES
1. Pontiac School District et al. v. Spellings, No. 09-71535 (E.D. Mich. Nov. 23, 2005).2. Connecticut v. Spellings, No. 05-1330 (D. Conn. Sept. 27, 2006).3. Available from http://www.pde.state.pa.us/k12_finances/site/default.asp.4. Available from http://www.pde.state.pa.us/a_and_t/cwp/view.asp?A=3&Q=115328.5. Available from http://www.pde.state.pa.us/k12_finances/site/default.asp.6. The calculation consists of three steps. First, the median tax share is calculated by dividing the
median value of housing by the total value of all housing in the district (source http://nces.ed.gov/sur-veys/sdds/shp2000.asp). The result is then multiplied by the number of students in the district (source ofdata: http://nces.ed.gov/surveys/sdds/shp2000.asp) to obtain the median tax share for students. The taxprice is the product of multiplying the median tax share for students by the residential percentage oftotal property taxes collected (source of data on property taxes: Excel file emailed to authors by thePennsylvania Department of Education).
7. Available from http://www.pde.state.pa.us/k12_finances/cwp/view.asp?a=11&Q=108503.8. Available from http://nces.ed.gov/surveys/sdds/geosupp.asp.9. Available from http://www.pde.state.pa.us/k12_finances/site/default.asp, http://www.pde.state.
pa.us/esl, http://nces.ed.gov/pubsearch/pubsinfo.asp? pubid=2007397, http://www.pde.state.pa.us/early_childhood/cwp/view.asp?a=316&q=125821.
10. 1 = less than 100, 2 = 101–300, 3 = 301–500, 4 = 501–700, 5 = 701–900, 6 = 901–1100, 7 =1101–1300, 8 = 1301–1500, 9 = 1501–2000, 10 = 2001–3000, 11 = 3001–4000, and 12 = 4001 and above.
11. Available from http://www.nces.ed.gov/edfin/.12. We examined this by means of chi-square comparison tests.
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APPENDIX
Questionnaire Items for Study Constructs and Associated MeasurementPropertiesa
Construct QuestionEFA
LoadingCFA
Loading Cronbach’s a
Formal Bridging In their official capacity as board members, how frequently do the members of your board interact with individuals in the following groups? (7-point scale: “never” to “daily”)
Local business leaders 0.637 0.715 0.757State legislators 0.606 0.502Local municipal government officials 0.674 0.703PTO/PTA leaders – –Civic community leaders – –Faith community leaders 0.544 –Ethnic/minority group representatives 0.524 –Seniors/retirees 0.608 0.548News/media representatives 0.517 –Youth group leaders 0.610 0.530Leaders in higher education 0.470 –Officials from other school districts 0.432 0.179
Informal Bridging
How frequently do the members of your board informally interact with the individuals in the following groups? (not in their official capacity, but in unofficial terms, due to contacts they may have with them in various other ways)
Local business leaders – – 0.775State legislators – –Local elected officials 0.515 –PTO/PTA leaders 0.576 0.562Civic community leaders 0.667 0.714Faith community leaders 0.616 0.618Ethnic/minority group representatives 0.517 0.499
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Seniors/retires – –News/media representatives 0.520 0.581Youth group leaders 0.648 –Leaders in higher education 0.756 0.631Officials form other school districts 0.571 –
Bonding-Shared Vision
For each of the following statements, to what extent do you agree or disagree? (5-point scale: “strongly disagree” to “strongly agree”)
Board members . . .Share the same ambitions and vision
for the school district0.683 0.809 0.912
Share a common view of the school district’s purpose in the community
0.535 0.773
Are committed to the goals of the school district
0.640 0.840
View themselves as a partner in charting the direction of the school district
0.583 0.762
Bonding-Information Exchange
For each of the following statements, to what extent do you agree or disagree?
Board members . . .Engage in open and honest communi-
cation with one another0.613 0.778 0.881
Do not have “hidden agendas” or issues 0.797 0.766Share and accept constructive criticism
without making it personal0.852 0.800
Discuss personal issues if they affect their job responsibilities at the school district
0.525 0.329
Willingly share information with one another
0.812 0.751
Keep each other informed all the time 0.768 0.756
Bonding-Trust For each of the following statements, to what extent do you agree or disagree?Board members. . .Can rely on each other in the school
district0.872 0.830 0.946
Have “team spirit’ 0.878 0.875Have confidence in one another 0.967 0.913Show a great deal of integrity 0.808 0.808Have a relationship built on trust and
respect0.933 0.897
Parent Support for Schooling
Indicate how strongly you agree or disagree with each statement below about parein support for your school district (5-point scale: “strongly dis-agree” to “strongly agree”)
Board members. . .Parents are well informed about my
district’s improvement efforts.0.519 0.570 0.785
Parents are active partners in my district’s improvement efforts.
0.698 0.694
(Continued)
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APPENDIX (Continued)
Construct QuestionEFA
LoadingCFA
Loading Cronbach’s a
Parents share in the responsibility for new strategies to involve parents in their children’s education.
0.737 0.741
Parents from all groups are involved in decision making with my school district.
0.590 0.701
The relationship between parents and my school district is one of respect and trust.
– –
a“-” indicates that the item was dropped from the measurement model.
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